neural network classifiers
Detection of Immunologically Significant Factors for Chronic Fatigue Syndrome Using Neural-Network Classifiers
... Neural-network classifiers were used to detect immunological differences in groups of chronic fatigue syndrome (CFS) patients that heretofore had not shown significant differences from ...Multilayer ...
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WAVELET ANALYSIS AND NEURAL NETWORK CLASSIFIERS TO DETECT MID-SAGITTAL SECTIONS FOR NUCHAL TRANSLUCENCY MEASUREMENT
... e-mail: {emanuela.orlandi, giuseppa.sciortino04, domenico.tegolo, cesare.valenti}@unipa.it (Received June 10, 2015; revised September 16, 2015; accepted February 1, 2016) ABSTRACT We propose a methodology to support the ...
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A Comparative Evaluation Study of Automated Gait Recognition based on Spatiotemporal Feature and Different Neural Network Classifiers
... supervised neural network classifiers were employed to classify the gaits based on the extracted ...learning neural networks are trained using four different gait cycles corresponding to four ...
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Combining Evolving Neural Network Classifiers Using Bagging
... The hest neural network is a single best individual network in the GA population without using bagging technique.. Also, the performance of simple combining methods [r] ...
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Adaptive Least Error Rate Algorithm for Neural Network Classifiers
... W e adopt the MER criterion and develop a stochastic gradient adaptive MER algorithm for training neural network classiers. W e employ an adap- tive strategy that is very similar to the one used for ...
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RECOGNITION OF HANDWRITTEN CHARACTER BASED ON WREDF AND NEURAL NETWORK CLASSIFIERS
... [email protected] Abstract: In this paper, efforts have been made to develop Automatic Handwritten Character Recognition System with high recognition accuracy, minimum training and classification time. The ...
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Class Disjointness Constraints as Specific Objective Functions in Neural Network Classifiers
... owl:DisjointClasses(leo:OfficerDriving leo:OfficerPassenger) With a large enough training data set, the constraints should be learnt by the model from the data. However, the dataset would need to cover all possible ...
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Self Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers
... years, neural network approaches, primarily RNNs and CNNs, have been the most suc- cessful for this ...of neural networks, self-attention net- works (SANs), have been created which uti- lizes the ...
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Neural Network Classifiers for Human Tissue Classification in NIR Biomedical Multispectral Imaging
... [20] Haiqiang Zuo, Heng Fan, Erik Blasch, and Haibin Ling. Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Processing Letters, 24(3):289–293, 2017. [21] Mart´ın Abadi, ...
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A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers
... [email protected] Abstract—This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN) based classifier. The originality of the proposed methodology, ...
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Detection of sodium oxalate needles in optical images using neural network classifiers
... If a General Regression Neural Network (GRNN) [4,5] is used as a classifier the number of training data points is taken to be in proportion with the a priori probability of occurre[r] ...
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Lane Change Prediction Using Gaussian Classification, Support Vector Classification and Neural Network Classifiers
... the classifiers is derived from the trajectory by selecting a subset of the features: lateral and longitudinal position coordinates, longitudinal acceleration, and ...
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Information Losses in Neural Classifiers From Sampling.
... I. I NTRODUCTION An estimator is limited to the information that it has about the variable it’s estimating. But this information is limited to what the estimator has seen from the samples training it. The full ...
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Interpreting Neural Network Hate Speech Classifiers
... For each word in our corpus, we construct a sen- tence of the form “they call you ” and feed it as input to the CNN-GRU network. We choose this structure to grammatically accommo- date both nouns and ...
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Neural Network For Face Recognition Using Different Classifiers
... Abstract: In this paper I proposed a novel technique for Face Detection and Recognition by Neural Network for Face Recognition using different Classifier. It is advance computer vision of human Face ...
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Prediction models in the design of neural network based ECG classifiers: A neural network and genetic programming approach
... How many nodes should each layer have? What activation function should be employed and in which configura- tion? Unfortunately, there is a shortage of evidence avail- able to designers that would enable them to make ...
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Ensemble Neural Network and K-NN Classifiers for Intrusion Detection
... weak classifiers ,as like decision tree algorithms can be changeable , specially when the designation of a point changes little and training can conduct to many different tree ...
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Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms
... Among all types of cancer, breast cancer is the most invasive cancer in women and presents a high mortality rate. Histopathological analysis is currently the most widely used method for breast cancer diagnosis. Thus, ...
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Detecting Denial of Service Attacks with Bayesian Classifiers and the Random Neural Network
... Bayesian Classifiers and the Random Neural Network G¨ulay ¨ Oke, George Loukas, Erol Gelenbe Abstract— Denial of Service (DoS) is a prevalent threat in today’s ...a network resource against it ...
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Integrating Predictions from Neural Network Relation Classifiers into Coreference and Bridging Resolution
... Universit¨at Stuttgart, Germany {roesigia,koepermn,nguyenkh,schulte}@ims.uni-stuttgart.de Abstract Cases of coreference and bridging resolution often require knowledge about semantic rela- tions between anaphors and ...
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